Technical Project Manager

Lilt---Lega-Italiana-Per-La-Lotta-Contro-I-Tumori-1; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

LILT is seeking a Technical Project Manager for our Applied AI team in the United States. You will lead large-scale multilingual data collection, human-in-the-loop workflows, and LLM evaluation initiatives, working with AI specialists, engineers, SMEs, and data operations teams to translate technical requirements into scalable data workflows.

The role blends technical project management with data analytics, unit-economics optimization, and hands-on evaluation processes central to modern LLM

Qualifications

• 3–5+ years of technical project management experience in AI/ML.
• Strong understanding of LLM training and evaluation concepts.
• Proficiency in SQL for analyzing delivery velocity and data quality.
• Experience with data pipelines, APIs, and validation processes.
• Experience partnering with engineers to manage requirements and dependencies.
• Ability to translate technical requirements for multilingual audiences.

Responsibilities

• Manage Applied AI data collection and evaluation projects from scoping through delivery.
• Translate AI requirements into structured data specifications and acceptance criteria.
• Manage timelines, dependencies, risks, and blockers across stakeholders.
• Coordinate data collection and annotation for SFT, RLHF, and model evaluation.
• Define and validate data workflows, tooling, and quality checks.
• Coordinate requirements for data formats, schemas, and metadata.
• Work with specialists to operationalize evaluation methods and issues resolution.
• Lead root-cause analysis on workflow bottlenecks and quality discrepancies.

Skills

Technical project management

AI/ML

SQL

Data pipelines

Agile/Scrum/Kanban

Multilingual collaboration

Communication

Tools

Jira

BI tools

Python

Job description

About LILT

AI is changing how the world communicates — and LILT is leading that transformation.

We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation, Canva, the United States Department of Defense, the United States Air Force, ASICS, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

Technical Project Manager — Applied AI

We are looking for a Technical Project Manager to join our Applied AI team and lead large-scale multilingual data collection, human-in-the-loop workflows, and Large Language Model (LLM) evaluation initiatives. You will work with AI specialists, engineering teams, specialized Subject Matter Experts (SMEs), and data operations teams to translate technical AI requirements into high-throughput data workflows, hybrid evaluation pipelines, and measurable delivery plans.

This role combines technical project management with hands-on data analysis, unit economics optimization, and a strong operational understanding of modern LLM training and evaluation processes.

Key Responsibilities

Technical Project Delivery

• Manage Applied AI data collection and evaluation projects from technical scoping through validation, delivery, and retrospective review. through implementation, validation, delivery, and retrospective review.
• Translate technical AI requirements into structured data specifications, domain-specific annotation requirements, evaluation plans, and acceptance criteria.
• Manage timelines and throughput velocity, dependencies, delivery risks, and technical blockers across Applied AI, engineering, and operations stakeholders.
• Coordinate data collection and annotation for supervised fine-tuning (SFT), preference data used in reinforcement learning from human feedback (RLHF), and model evaluation.

Data Workflows & Evaluation

• Partner with engineering teams to define and validate data workflows, including ingestion, annotation tooling, systems integrations, quality checks, data generation pipelines, and delivery.
• Operationalize hybrid evaluation methodologies, combining human review with automated evaluation tooling, and LLM-as-a-judge frameworks.
• Coordinate requirements for data formats, schemas, metadata, annotation tools, and system integrations.
• Coordinate requirements for data formats, schemas, metadata, annotation tooling, and automated quality checks.
• Work with Applied AI specialists to operationalize evaluation methods, including human review, response ranking, and safety testing.
• Investigate data, pipeline, or tooling issues and coordinate prompt resolution with technical owners.

Quality, Cost, & Performance

• Use SQL and business intelligence tools to analyze throughput velocity, quality, unit economics, and supplier/delivery performance.
• Define and monitor quality standards, including inter-annotator (IAA) agreement, gold-set performance, and dataset completeness.
• Lead root-cause analysis on quality discrepancies, workflow bottlenecks, benchmark discrepancies, and quality edge cases.
• Establish delivery validation checks and coordinate stakeholder acceptance of completed, accurate datasets, delivery targets, and evaluation results.

Stakeholder & Contributor Management

• Translate complex technical requirements into actionable plans and clear instructions for global contributor teams, external vendors, and specialized domain SMEs and teams.
• Communicate project status, technical risks and tradeoffs, unit economics, and operational risks to technical and non-technical stakeholders.
• Ensure contributor feedback and evaluation findings inform improvements to internal tooling, guidelines, and dataset pipelines.

Essential Qualifications

• 3–5+ years of technical project management experience in AI/ML, data platforms, or technical data operations.
• Strong understanding of LLM training and evaluation concepts, including SFT, RLHF, human evaluation, automated evaluations, LLM-as-a-judge, and red teaming.
• Proficiency in SQL for analyzing delivery velocity, data quality metrics, and cost structures.
• Working knowledge of data pipelines, structured data formats, APIs, and validation processes.
• Experience partnering with engineers to manage technical requirements, dependencies, and issue resolution.
• Proven ability to track and optimize project unit economics (cost per token/task) and delivery velocity.
• Strong communication skills, including the ability to translate technical requirements for multilingual audiences and diverse global contributor networks
• Experience managing complex workflows using Agile, Scrum, or Kanban.

Preferred Qualifications

• Experience using Python or scripting tools for data analysis, workflow automation, or evaluation scripting.
• Experience managing high-skill Subject Matter Experts (SMEs) or specialized contributor networks across technical domains.
• Experience with annotation platforms, automated evaluation tools, business intelligence tools, and Jira.
• Experience delivering complex multilingual or multimodal data projects.
• Background in computer science, data science, engineering, or equivalent practical experience.
• Fluency in an additional language.

Our Story

Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born.

LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.

With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning.

Our Tech

What sets our platform